Measurement exploratory data analysis¶
DEVICES = [
'beaglebone-fan',
'beaglebone-compressor',
'beaglebone-pump',
'beaglebone-refrigerator',
'mafaulda-a',
'mafaulda-b'
]
DEVICE = DEVICES[5]
T_WAVEFORM = 1 # (1 = MaufaulDa, 5 = others)
T_SEC = T_WAVEFORM
NFFT = 2**14 # (2**10 = MaufaulDa, 2**14 = others)
F_LIMIT = 3000 # (3000 = MaufaulDa, None = others)
import os
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
from tabulate import tabulate
from IPython.display import Markdown, HTML
from tqdm.notebook import tqdm
from typing import List, Tuple
from scipy.signal import find_peaks, butter, lfilter
from tsfel.feature_extraction.features import fundamental_frequency
from zipfile import ZipFile
import sys
sys.path.append('../')
from vibrodiagnostics import mafaulda, selection, discovery, models
def beaglebone_measurement(filename: str, fs: int) -> Tuple[str, pd.DataFrame]:
g = 9.81
milivolts = 1800
resolution = 2**12
columns = ['x', 'y', 'z']
ts = pd.read_csv(filename, delimiter='\t', index_col=False, header=None, names=columns)
# Calculate amplitude in m/s^2 Beaglebone Black ADC and ADXL335 resolution (VIN 1.8V, 12bits)
for dim in columns:
ts[dim] = ts[dim] * (milivolts / resolution) # ADC to mV
ts[dim] = (ts[dim] / 180) * g # mV to m/s^2 (180 mV/g)
ts[dim] -= ts[dim].mean()
ts['t'] = ts.index * (1 / fs)
ts.set_index('t', inplace=True)
return (os.path.basename(filename), ts, fs, ts.columns) # last is feature columns
def beaglebone_dataset(filenames: List[str], fs: int) -> List[Tuple[str, pd.DataFrame]]:
dataset = []
for filename in filenames:
name, ts, fs, cols = beaglebone_measurement(filename, fs)
dataset.append((name, ts))
return dataset
def lowpass_filter(data, cutpoint, fs, order=5):
b, a = butter(order, cutpoint, fs=fs, btype='lowpass')
y = lfilter(b, a, data)
return y
def mafaulda_dataset(
place,
features_path = '../../datasets/features_data/',
mafaulda_path='../../datasets/MAFAULDA.zip',
rpm=2500,
lowpass_hz=10000):
metadata_filename = os.path.join(features_path, selection.MAFAULDA_METADATA)
faults = {
'A': {
'normal': 'normal',
'imbalance': 'imbalance',
'horizontal-misalignment': 'misalignment',
'vertical-misalignment': 'misalignment',
'underhang-outer_race': 'outer race fault',
'underhang-cage_fault': 'cage fault',
'underhang-ball_fault': 'ball fault'
},
'B': {
'normal': 'normal',
'imbalance': 'imbalance',
'horizontal-misalignment': 'misalignment',
'vertical-misalignment': 'misalignment',
'overhang-cage_fault': 'cage fault',
'overhang-ball_fault': 'ball fault',
'overhang-outer_race': 'outer race fault'
}
}
bearings = {
'A': ['ax', 'ay', 'az'],
'B': ['bx', 'by', 'bz']
}
metadata = pd.read_csv(metadata_filename, index_col='filename')
metadata.reset_index(inplace=True)
metadata = metadata[metadata['fault'].isin(tuple(faults[place]))]
metadata = models.fault_labeling(metadata, faults[place])
files = pd.DataFrame()
# Worst severity and mid rpm
for name, group in metadata[(metadata['rpm'] >= rpm)].groupby(by='fault', observed=False):
files = pd.concat([
files,
group[
group['severity_level'] == group['severity_level'].max()
].sort_values(by='rpm', ascending=True).head(1)
])
ordering = {
'normal': 0,
'misalignment': 1,
'imbalance': 2,
'cage fault': 3,
'ball fault': 4,
'outer race fault': 5,
}
source = ZipFile(mafaulda_path)
dataset = len(files) * [0]
for index, file in files.iterrows():
ts = mafaulda.csv_import(source, file['filename'])
ts = ts[bearings[place]]
ts.columns = ts.columns.str.extract(r'(\w)$')[0]
for axis in ts.columns:
ts[axis] = lowpass_filter(ts[axis], lowpass_hz, file['fs'])
pos = ordering[file['fault']]
dataset[pos] = ((file['fault'] + ' (' + file['filename'] +')', ts))
return dataset
Load dataset
if DEVICE == 'beaglebone-fan':
Fs = 2500
path = '../../inspections/fan/'
files = [
'1_still.tsv', '2_still.tsv', '3_still.tsv',
'1_up.tsv', '2_up.tsv', '3_up.tsv',
'1_down.tsv', '2_down.tsv', '3_down.tsv'
]
files = [os.path.join(path, name) for name in files]
DATASET = beaglebone_dataset(files, Fs)
elif DEVICE == 'beaglebone-compressor':
Fs = 2500
path = '../../inspections/datacentres/shc3/'
files = [
'k3_1.tsv', 'k3_2.tsv', 'k3_3.tsv', 'k3_4.tsv',
'k5_1.tsv', 'k5_2.tsv', 'k5_3.tsv', 'k5_4.tsv'
]
files = [os.path.join(path, name) for name in files]
DATASET = beaglebone_dataset(files, Fs)
elif DEVICE == 'beaglebone-pump':
Fs = 2500
path = '../../inspections/bvs/'
files = [
'bvs_1_hore.tsv', 'bvs_2_hore.tsv'
#, 'bvs_3_motor.tsv', 'bvs_4_motor.tsv'
]
files = [os.path.join(path, name) for name in files]
DATASET = beaglebone_dataset(files, Fs)
elif DEVICE == 'beaglebone-refrigerator':
Fs = 2500
path = '../../inspections/home-refrigerator/'
files = [
'ch1.tsv', 'ch2.tsv', 'ch3.tsv', 'ch4.tsv', 'ch5.tsv'
]
files = [os.path.join(path, name) for name in files]
DATASET = beaglebone_dataset(files, Fs)
elif DEVICE == 'mafaulda-a':
Fs = mafaulda.FS_HZ
DATASET = mafaulda_dataset(place='A')
elif DEVICE == 'mafaulda-b':
Fs = mafaulda.FS_HZ
DATASET = mafaulda_dataset(place='B')
for name, ts in DATASET:
display(Markdown(f'**{name}**'))
ts.info()
print()
normal (normal/43.6224.csv)
<class 'pandas.core.frame.DataFrame'> Index: 250000 entries, 0.0 to 4.999980000000001 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 x 250000 non-null float64 1 y 250000 non-null float64 2 z 250000 non-null float64 dtypes: float64(3) memory usage: 7.6 MB
misalignment (horizontal-misalignment/2.0mm/42.5984.csv)
<class 'pandas.core.frame.DataFrame'> Index: 250000 entries, 0.0 to 4.999980000000001 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 x 250000 non-null float64 1 y 250000 non-null float64 2 z 250000 non-null float64 dtypes: float64(3) memory usage: 7.6 MB
imbalance (imbalance/35g/43.6224.csv)
<class 'pandas.core.frame.DataFrame'> Index: 250000 entries, 0.0 to 4.999980000000001 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 x 250000 non-null float64 1 y 250000 non-null float64 2 z 250000 non-null float64 dtypes: float64(3) memory usage: 7.6 MB
cage fault (overhang/cage_fault/35g/43.008.csv)
<class 'pandas.core.frame.DataFrame'> Index: 250000 entries, 0.0 to 4.999980000000001 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 x 250000 non-null float64 1 y 250000 non-null float64 2 z 250000 non-null float64 dtypes: float64(3) memory usage: 7.6 MB
ball fault (overhang/ball_fault/20g/36.0448.csv)
<class 'pandas.core.frame.DataFrame'> Index: 250000 entries, 0.0 to 4.999980000000001 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 x 250000 non-null float64 1 y 250000 non-null float64 2 z 250000 non-null float64 dtypes: float64(3) memory usage: 7.6 MB
outer race fault (overhang/outer_race/35g/43.2128.csv)
<class 'pandas.core.frame.DataFrame'> Index: 250000 entries, 0.0 to 4.999980000000001 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 x 250000 non-null float64 1 y 250000 non-null float64 2 z 250000 non-null float64 dtypes: float64(3) memory usage: 7.6 MB
for name, ts in DATASET:
display(Markdown(f'**{name}**'))
display(tabulate(ts.describe(), headers='keys', tablefmt='html'))
ts.boxplot(grid=True)
plt.show()
normal (normal/43.6224.csv)
| x | y | z | |
|---|---|---|---|
| count | 250000 | 250000 | 250000 |
| mean | 0.00442377 | 0.00256424 | 0.00729225 |
| std | 0.628605 | 0.0284248 | 0.409702 |
| min | -2.48307 | -0.0753402 | -1.50922 |
| 25% | -0.408632 | -0.0203851 | -0.279686 |
| 50% | 0.0140406 | 0.00369867 | -0.00664152 |
| 75% | 0.434552 | 0.0257897 | 0.281814 |
| max | 2.06348 | 0.12156 | 2.09294 |
misalignment (horizontal-misalignment/2.0mm/42.5984.csv)
| x | y | z | |
|---|---|---|---|
| count | 250000 | 250000 | 250000 |
| mean | 0.0091607 | 0.00282899 | 0.00110133 |
| std | 0.108023 | 0.0304018 | 0.481472 |
| min | -0.453353 | -0.108466 | -1.76433 |
| 25% | -0.0582322 | -0.0188245 | -0.336619 |
| 50% | 0.0179518 | 0.00321832 | -0.0209285 |
| 75% | 0.0850128 | 0.025359 | 0.31991 |
| max | 0.322083 | 0.0957236 | 2.19127 |
imbalance (imbalance/35g/43.6224.csv)
| x | y | z | |
|---|---|---|---|
| count | 250000 | 250000 | 250000 |
| mean | 0.00812089 | 0.0028024 | 0.0175564 |
| std | 0.199627 | 0.0420892 | 1.9319 |
| min | -0.761782 | -0.147555 | -4.2895 |
| 25% | -0.125994 | -0.0262874 | -1.70635 |
| 50% | 0.0210197 | 0.00320514 | -0.0998429 |
| 75% | 0.155579 | 0.0328518 | 1.77266 |
| max | 0.513142 | 0.136843 | 4.5501 |
cage fault (overhang/cage_fault/35g/43.008.csv)
| x | y | z | |
|---|---|---|---|
| count | 250000 | 250000 | 250000 |
| mean | 0.0782755 | 0.00476235 | 0.0240576 |
| std | 1.57258 | 0.489683 | 1.94964 |
| min | -3.40253 | -1.30722 | -3.30159 |
| 25% | -1.20947 | -0.344713 | -1.82606 |
| 50% | -0.0172133 | 0.0869372 | -0.178371 |
| 75% | 1.32615 | 0.470354 | 1.81411 |
| max | 4.26328 | 0.649884 | 4.78323 |
ball fault (overhang/ball_fault/20g/36.0448.csv)
| x | y | z | |
|---|---|---|---|
| count | 250000 | 250000 | 250000 |
| mean | 0.0302217 | 0.0155812 | -0.000449872 |
| std | 1.5222 | 2.99196 | 1.80557 |
| min | -4.78097 | -9.09082 | -7.13457 |
| 25% | -1.122 | -2.14442 | -1.26887 |
| 50% | 0.130627 | 0.558218 | -0.052664 |
| 75% | 1.0827 | 2.38637 | 1.39874 |
| max | 4.67537 | 5.6701 | 4.50713 |
outer race fault (overhang/outer_race/35g/43.2128.csv)
| x | y | z | |
|---|---|---|---|
| count | 250000 | 250000 | 250000 |
| mean | 0.0438275 | 0.00369081 | 0.0156163 |
| std | 1.05157 | 0.253477 | 1.90162 |
| min | -2.86968 | -0.953974 | -4.21464 |
| 25% | -0.725317 | -0.164062 | -1.71735 |
| 50% | -0.0285322 | 0.0863153 | -0.00410187 |
| 75% | 0.779466 | 0.21807 | 1.68526 |
| max | 3.5737 | 0.311572 | 4.68837 |
Statistical tests
- Normality test: Kolmogorov–Smirnov test
- Normality visual test: Quantile-quantile plot on chosen recording
- Stationarity test: Augmented Dickey–Fuller test
- Stationarity visual test: Autocorrelation plot
from statsmodels.tsa.stattools import adfuller
from statsmodels.api import qqplot
from scipy.stats import kstest
normality_tests = []
for name, ts in DATASET:
for x in ts.columns:
p_value = kstest(ts[x], 'norm').pvalue
test = {'name': name, 'axis': x, 'p-value': p_value, 'not-normal': p_value < 0.05}
normality_tests.append(test)
normality_tests = pd.DataFrame.from_records(normality_tests)
print(normality_tests.value_counts('not-normal'))
normality_tests.describe()
not-normal True 18 Name: count, dtype: int64
| p-value | |
|---|---|
| count | 18.0 |
| mean | 0.0 |
| std | 0.0 |
| min | 0.0 |
| 25% | 0.0 |
| 50% | 0.0 |
| 75% | 0.0 |
| max | 0.0 |
name, ts = DATASET[0]
fig, ax = plt.subplots(1, len(ts.columns), figsize=(10, 4))
for i, x in enumerate(ts.columns):
qqplot(ts[x], line='45', ax=ax[i], marker='.', alpha=0.5)
ax[i].set_title(f'Axis: {x}')
plt.tight_layout()
print(name)
plt.show()
normal (normal/43.6224.csv)
stationarity_tests = []
for name, ts in tqdm(DATASET):
for x in ts.columns:
result = adfuller(ts[x].loc[T_WAVEFORM:T_WAVEFORM+1])
p_value = result[1]
test = {
'name': name,
'axis': x,
'statistic': result[0],
'p-value': p_value,
'stationary': p_value < 0.001
}
stationarity_tests.append(test)
stationarity_tests = pd.DataFrame.from_records(stationarity_tests)
print(stationarity_tests.value_counts('stationary'))
stationarity_tests['p-value'].describe()
0%| | 0/6 [00:00<?, ?it/s]
stationary True 18 Name: count, dtype: int64
count 1.800000e+01 mean 5.255316e-15 std 1.883517e-14 min 0.000000e+00 25% 1.288097e-29 50% 2.940757e-23 75% 2.062478e-18 max 7.956672e-14 Name: p-value, dtype: float64
name, ts = DATASET[0]
fig, ax = plt.subplots(1, len(ts.columns), figsize=(10, 4))
for i, x in enumerate(ts.columns):
ax[i].acorr(ts[x], maxlags=50)
ax[i].set_title(f'Axis: {x}')
plt.tight_layout()
print(name)
plt.show()
normal (normal/43.6224.csv)
Time domain histogram
for name, ts in DATASET:
display(Markdown(f'**{name}**'))
axis = ts.columns
ax = ts[axis].hist(figsize=(15, 3), grid=True, bins=100, layout=(1, 3), edgecolor='black', linewidth=0.5)
plt.show()
normal (normal/43.6224.csv)
misalignment (horizontal-misalignment/2.0mm/42.5984.csv)
imbalance (imbalance/35g/43.6224.csv)
cage fault (overhang/cage_fault/35g/43.008.csv)
ball fault (overhang/ball_fault/20g/36.0448.csv)
outer race fault (overhang/outer_race/35g/43.2128.csv)
Time domain waveform
for name, ts in DATASET:
display(Markdown(f'**{name}**'))
axis = ts.columns
ax = ts[axis].plot(figsize=(20, 8), grid=True, subplots=True)
for i, axname in enumerate(axis):
ax[i].set_xlabel('Time [s]')
ax[i].set_ylabel(f'Amplitude ({axname}) [m/s^2]')
plt.show() # plt.savefig('waveform.png')
normal (normal/43.6224.csv)
misalignment (horizontal-misalignment/2.0mm/42.5984.csv)
imbalance (imbalance/35g/43.6224.csv)
cage fault (overhang/cage_fault/35g/43.008.csv)
ball fault (overhang/ball_fault/20g/36.0448.csv)
outer race fault (overhang/outer_race/35g/43.2128.csv)
Time domain waveform zoom detail
for name, ts in DATASET:
axis = ts.columns
display(Markdown(f'**{name}**'))
ax = (ts[axis].iloc[int(T_WAVEFORM*Fs):int(T_WAVEFORM*Fs)+Fs]
.plot(figsize=(20, 10), grid=True, subplots=True))
for i, axname in enumerate(axis):
ax[i].set_xlabel('Time [s]')
ax[i].set_ylabel(f'Amplitude ({axname}) [m/s^2]')
plt.show() # plt.savefig('waveform_zoom.png')
normal (normal/43.6224.csv)
misalignment (horizontal-misalignment/2.0mm/42.5984.csv)
imbalance (imbalance/35g/43.6224.csv)
cage fault (overhang/cage_fault/35g/43.008.csv)
ball fault (overhang/ball_fault/20g/36.0448.csv)
outer race fault (overhang/outer_race/35g/43.2128.csv)
Time domain waveform zoom - faults side by side
fig, ax = plt.subplots(len(DATASET), 3, figsize=(12, 15), sharex=True)
for idx, df in enumerate(DATASET):
name, ts = df
columns = ts.columns
ax[idx][1].set_title(name)
ax[idx][0].set_ylabel('Amplitude [m/s^2]')
for pos, axis in enumerate(columns):
data = ts[axis].loc[T_WAVEFORM:T_WAVEFORM+0.3]
ax[idx][pos].plot(data.index, data, linewidth=1, color='darkblue')
ax[idx][pos].grid()
plt.tight_layout()
plt.show()
def spectogram(x, debug=True):
fig, ax = plt.subplots(figsize=(15, 4))
cmap = plt.get_cmap('inferno')
pxx, freqs, t, im = plt.specgram(
x, NFFT=NFFT, Fs=Fs,
detrend='mean',
mode='magnitude', scale='dB',
cmap=cmap, vmin=-60
)
fig.colorbar(im, aspect=20, pad=0.04)
ax.set_xlabel('Time [s]')
ax.set_ylabel('Frequency [Hz]')
mafaulda.resolution_calc(Fs, NFFT)
return freqs, pxx
def window_idx(t):
return (Fs * t) // NFFT + 1
def spectrum_slice(freqs, Pxx, t):
fig, ax = plt.subplots(2, 1, figsize=(20, 8))
n = window_idx(t)
dB = 20 * np.log10(Pxx.T[n] / 0.000001)
ax[0].plot(freqs, dB) # 1 dB = 1 um/s^2
ax[0].grid(True)
ax[0].set_xlabel('Frequency [Hz]')
ax[0].set_ylabel('Amplitude [dB]')
ax[1].plot(freqs, Pxx.T[n])
ax[1].grid(True)
ax[1].set_xlabel('Frequency [Hz]')
ax[1].set_ylabel('Amplitude [m/s^2]')
return n
def get_max_frequency(freqs, Pxx, i):
max_freq = freqs[np.argmax(Pxx.T[i])]
return max_freq
def get_peaks(freqs, Pxx, i, top=5):
amplitudes = Pxx.T[i]
peaks, _ = find_peaks(amplitudes, distance=3)
fundamental = get_max_frequency(freqs, Pxx, i)
f_top = freqs[peaks[np.argsort(amplitudes[peaks])]][::-top]
y_top = np.sort(amplitudes[peaks])[::-top]
return pd.DataFrame({
'f': f_top,
'y': y_top,
'1x': f_top / fundamental
})
def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
b, a = butter(order, [lowcut, highcut], fs=fs, btype='band')
y = lfilter(b, a, data)
return y
def get_spectrograms(DATASET: List[pd.DataFrame], axis: str) -> list:
spectrograms = []
for name, ts in DATASET:
base_freq = fundamental_frequency(ts[axis], Fs)
display(Markdown(f'**{name}** *({axis.upper()} axis, Fundamental = {base_freq:.4f} Hz)*'))
freqs, Pxx = spectogram(ts[axis])
spectrograms.append((name, freqs, Pxx))
plt.show() # plt.savefig(f'x_axis_fft_{NFFT}.png')
return spectrograms
def show_spectrogram_detail(spectrograms: list, axis: str, t: float):
for name, freqs, Pxx in spectrograms:
display(Markdown(f'**{name}** ({axis.upper()} axis @ {t}s)'))
i_window = spectrum_slice(freqs, Pxx, t)
plt.show() #plt.savefig(f'x_axis_fft_{NFFT}_at_{T_SEC}s.png')
def show_mms_peaks(spectrograms: list, axis: str, t: float):
for name, freqs, Pxx in spectrograms:
display(Markdown(f'**{name}** ({axis.upper()} axis @ {t}s)'))
i_window = window_idx(t)
peaks = discovery.mms_peak_finder(Pxx.T[i_window])
fig, ax = plt.subplots(1, 1, figsize=(15, 3))
ax.grid(True)
ax.plot(freqs, Pxx.T[i_window])
ax.scatter(freqs[peaks], Pxx.T[i_window][peaks], marker='^', color='red')
ax.set_xlabel('Frequency [Hz]')
plt.show()
def show_harmonic_series(spectrograms: list, axis: str, t: float):
# https://stackoverflow.com/questions/1982770/changing-the-color-of-an-axis
for name, freqs, Pxx in spectrograms:
display(Markdown(f'**{name}** ({axis.upper()} axis @ {t}s)'))
i_window = window_idx(t)
h_series = discovery.harmonic_series_detection(freqs, Pxx.T[i_window], Fs, NFFT)
# Find best (sum of harmonics' amplitudes in the largest)
max_harmonic_amp_idx = np.argmax([
sum([h[1] for h in s]) / len(s)
for s in h_series
])
best_harmonic_series = pd.DataFrame(
h_series[max_harmonic_amp_idx],
columns=['Frequency [Hz]', 'Amplitude [m/s^2]']
)
best_harmonic_series.index += 1
display(tabulate(best_harmonic_series, headers='keys', tablefmt='html'))
# Plot found harmonic series
fig, ax = plt.subplots(1, 8, figsize=(30, 4))
for i in range(8):
s = h_series[i+1]
if i == max_harmonic_amp_idx:
ax[i].xaxis.label.set_color('red')
ax[i].plot(freqs, Pxx.T[i_window])
ax[i].scatter([x[0] for x in s], [x[1] for x in s], marker='^', color='red')
ax[i].set_xlabel('Frequency [Hz]')
plt.show()
def show_spectra_largest_amplitudes(spectrograms: list, axis: str, t: float):
for name, freqs, Pxx in spectrograms:
display(Markdown(f'**{name}** ({axis.upper()} axis @ {t}s)'))
i_window = window_idx(t)
x_fundamental = get_max_frequency(freqs, Pxx, i_window)
peaks = get_peaks(freqs, Pxx, i_window)
display(Markdown(f'- *Fundamental frequency:* {x_fundamental} Hz'))
display(tabulate(peaks.head(5), headers='keys', tablefmt='html'))
def compare_limited_specrograms(spectrograms: list, axis: str, t: float):
fig, ax = plt.subplots(len(DATASET), 1, figsize=(20, 20), sharey=True)
i = 0
for name, ts in DATASET:
signal = ts[axis].loc[t:t+NFFT/Fs].to_numpy()
pxx = np.abs(np.fft.rfft(signal) / len(signal))
freqs = np.fft.fftfreq(len(signal), d=1/Fs)[:len(pxx)]
#ilast = len(freqs[freqs < F_LIMIT])
ax[i].plot(freqs, pxx)
ax[i].grid(True)
ax[i].set_xlabel('Frequency [Hz]')
ax[i].set_ylabel('Amplitude [m/s^2]')
ax[i].set_xlim(0, F_LIMIT)
ax[i].set_ylim(0, 0.4)
ax[i].set_title(name)
i += 1
def spectrogram_energy_left_cumulative(spectrograms: list, axis: str, t: float):
fig, ax = plt.subplots(len(DATASET), 1, figsize=(20, 20), sharey=True)
i = 0
for name, ts in DATASET:
signal = ts[axis].loc[t:t+NFFT/Fs].to_numpy()
pxx = np.abs(np.fft.rfft(signal) / len(signal))
freqs = np.fft.fftfreq(len(signal), d=1/Fs)[:len(pxx)]
ax[i].plot(freqs, np.cumsum(pxx) / np.sum(pxx))
ax[i].grid(True)
ax[i].set_xlabel('Frequency [Hz]')
ax[i].set_ylabel('Cumulative energy [%]')
#ax[i].set_xlim(0, 10000)
ax[i].set_title(name)
i += 1
Compare mafaulda faults
compare_limited_specrograms(DATASET, 'x', T_SEC)
plt.tight_layout()
plt.show()
compare_limited_specrograms(DATASET, 'y', T_SEC)
plt.tight_layout()
plt.show()
compare_limited_specrograms(DATASET, 'z', T_SEC)
plt.tight_layout()
plt.show()
Compare cumulative sums
spectrogram_energy_left_cumulative(DATASET, 'x', T_SEC)
plt.tight_layout()
plt.show()
spectrogram_energy_left_cumulative(DATASET, 'y', T_SEC)
plt.tight_layout()
plt.show()
spectrogram_energy_left_cumulative(DATASET, 'z', T_SEC)
plt.tight_layout()
plt.show()
Spectrogram in X axis
x_spectra = get_spectrograms(DATASET, 'x')
normal (normal/43.6224.csv) (X axis, Fundamental = 1.0000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
misalignment (horizontal-misalignment/2.0mm/42.5984.csv) (X axis, Fundamental = 3.2000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
imbalance (imbalance/35g/43.6224.csv) (X axis, Fundamental = 42.6000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
cage fault (overhang/cage_fault/35g/43.008.csv) (X axis, Fundamental = 42.0000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
ball fault (overhang/ball_fault/20g/36.0448.csv) (X axis, Fundamental = 2.4000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
outer race fault (overhang/outer_race/35g/43.2128.csv) (X axis, Fundamental = 42.2000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
Spectrogram detail in X axis
show_spectrogram_detail(x_spectra, 'x', T_SEC)
normal (normal/43.6224.csv) (X axis @ 1s)
misalignment (horizontal-misalignment/2.0mm/42.5984.csv) (X axis @ 1s)
imbalance (imbalance/35g/43.6224.csv) (X axis @ 1s)
cage fault (overhang/cage_fault/35g/43.008.csv) (X axis @ 1s)
ball fault (overhang/ball_fault/20g/36.0448.csv) (X axis @ 1s)
outer race fault (overhang/outer_race/35g/43.2128.csv) (X axis @ 1s)
Peaks in frequency spectrum in X axis
- MMS peak finder algorithm
show_mms_peaks(x_spectra, 'x', T_SEC)
normal (normal/43.6224.csv) (X axis @ 1s)
misalignment (horizontal-misalignment/2.0mm/42.5984.csv) (X axis @ 1s)
imbalance (imbalance/35g/43.6224.csv) (X axis @ 1s)
cage fault (overhang/cage_fault/35g/43.008.csv) (X axis @ 1s)
ball fault (overhang/ball_fault/20g/36.0448.csv) (X axis @ 1s)
outer race fault (overhang/outer_race/35g/43.2128.csv) (X axis @ 1s)
Harmonic series detection in X axis
# show_harmonic_series(x_spectra, 'x', T_SEC)
show_spectra_largest_amplitudes(x_spectra, 'x', T_SEC)
normal (normal/43.6224.csv) (X axis @ 1s)
- Fundamental frequency: 253.29589843749997 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 253.296 | 0.178571 | 1 |
| 1 | 3.05176 | 0.0797164 | 0.0120482 |
| 2 | 274.658 | 0.0583556 | 1.08434 |
| 3 | 360.107 | 0.0343078 | 1.42169 |
| 4 | 555.42 | 0.0267719 | 2.19277 |
misalignment (horizontal-misalignment/2.0mm/42.5984.csv) (X axis @ 1s)
- Fundamental frequency: 9.155273437499998 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 9.15527 | 0.0321657 | 1 |
| 1 | 125.122 | 0.0114684 | 13.6667 |
| 2 | 18.3105 | 0.0070992 | 2 |
| 3 | 708.008 | 0.00466999 | 77.3333 |
| 4 | 466.919 | 0.00390644 | 51 |
imbalance (imbalance/35g/43.6224.csv) (X axis @ 1s)
- Fundamental frequency: 42.72460937499999 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 42.7246 | 0.104049 | 1 |
| 1 | 253.296 | 0.0174547 | 5.92857 |
| 2 | 338.745 | 0.0091005 | 7.92857 |
| 3 | 234.985 | 0.00612845 | 5.5 |
| 4 | 158.691 | 0.00422818 | 3.71429 |
cage fault (overhang/cage_fault/35g/43.008.csv) (X axis @ 1s)
- Fundamental frequency: 42.72460937499999 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 42.7246 | 0.829558 | 1 |
| 1 | 3.05176 | 0.199422 | 0.0714286 |
| 2 | 115.967 | 0.0491828 | 2.71429 |
| 3 | 292.969 | 0.025542 | 6.85714 |
| 4 | 546.265 | 0.0137255 | 12.7857 |
ball fault (overhang/ball_fault/20g/36.0448.csv) (X axis @ 1s)
- Fundamental frequency: 347.90039062499994 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 347.9 | 0.647856 | 1 |
| 1 | 207.52 | 0.191531 | 0.596491 |
| 2 | 137.329 | 0.139324 | 0.394737 |
| 3 | 488.281 | 0.0564564 | 1.40351 |
| 4 | 357.056 | 0.0440482 | 1.02632 |
outer race fault (overhang/outer_race/35g/43.2128.csv) (X axis @ 1s)
- Fundamental frequency: 42.72460937499999 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 42.7246 | 0.447526 | 1 |
| 1 | 3.05176 | 0.116225 | 0.0714286 |
| 2 | 115.967 | 0.0666007 | 2.71429 |
| 3 | 198.364 | 0.0373502 | 4.64286 |
| 4 | 631.714 | 0.0242998 | 14.7857 |
Spectrogram in Y axis
y_spectra = get_spectrograms(DATASET, 'y')
normal (normal/43.6224.csv) (Y axis, Fundamental = 4.6000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
misalignment (horizontal-misalignment/2.0mm/42.5984.csv) (Y axis, Fundamental = 3.2000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
imbalance (imbalance/35g/43.6224.csv) (Y axis, Fundamental = 10.6000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
cage fault (overhang/cage_fault/35g/43.008.csv) (Y axis, Fundamental = 42.0000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
ball fault (overhang/ball_fault/20g/36.0448.csv) (Y axis, Fundamental = 35.2000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
outer race fault (overhang/outer_race/35g/43.2128.csv) (Y axis, Fundamental = 42.2000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
Spectrogram detail in Y axis
show_spectrogram_detail(y_spectra, 'y', T_SEC)
normal (normal/43.6224.csv) (Y axis @ 1s)
misalignment (horizontal-misalignment/2.0mm/42.5984.csv) (Y axis @ 1s)
imbalance (imbalance/35g/43.6224.csv) (Y axis @ 1s)
cage fault (overhang/cage_fault/35g/43.008.csv) (Y axis @ 1s)
ball fault (overhang/ball_fault/20g/36.0448.csv) (Y axis @ 1s)
outer race fault (overhang/outer_race/35g/43.2128.csv) (Y axis @ 1s)
Peaks in frequency spectrum in Y axis
show_mms_peaks(y_spectra, 'y', T_SEC)
normal (normal/43.6224.csv) (Y axis @ 1s)
misalignment (horizontal-misalignment/2.0mm/42.5984.csv) (Y axis @ 1s)
imbalance (imbalance/35g/43.6224.csv) (Y axis @ 1s)
cage fault (overhang/cage_fault/35g/43.008.csv) (Y axis @ 1s)
ball fault (overhang/ball_fault/20g/36.0448.csv) (Y axis @ 1s)
outer race fault (overhang/outer_race/35g/43.2128.csv) (Y axis @ 1s)
Harmonic series detection in Y axis
# show_harmonic_series(y_spectra, 'y', T_SEC)
show_spectra_largest_amplitudes(y_spectra, 'y', T_SEC)
normal (normal/43.6224.csv) (Y axis @ 1s)
- Fundamental frequency: 42.72460937499999 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 42.7246 | 0.00383932 | 1 |
| 1 | 97.6562 | 0.00269748 | 2.28571 |
| 2 | 61.0352 | 0.00229201 | 1.42857 |
| 3 | 466.919 | 0.00211743 | 10.9286 |
| 4 | 73.2422 | 0.00181217 | 1.71429 |
misalignment (horizontal-misalignment/2.0mm/42.5984.csv) (Y axis @ 1s)
- Fundamental frequency: 9.155273437499998 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 9.15527 | 0.00710433 | 1 |
| 1 | 122.07 | 0.00281931 | 13.3333 |
| 2 | 94.6045 | 0.0021457 | 10.3333 |
| 3 | 143.433 | 0.00198247 | 15.6667 |
| 4 | 18.3105 | 0.00187833 | 2 |
imbalance (imbalance/35g/43.6224.csv) (Y axis @ 1s)
- Fundamental frequency: 42.72460937499999 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 42.7246 | 0.0171923 | 1 |
| 1 | 210.571 | 0.00400206 | 4.92857 |
| 2 | 234.985 | 0.00266793 | 5.5 |
| 3 | 299.072 | 0.00210059 | 7 |
| 4 | 338.745 | 0.0018251 | 7.92857 |
cage fault (overhang/cage_fault/35g/43.008.csv) (Y axis @ 1s)
- Fundamental frequency: 42.72460937499999 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 42.7246 | 0.34031 | 1 |
| 1 | 210.571 | 0.0167542 | 4.92857 |
| 2 | 421.143 | 0.0108792 | 9.85714 |
| 3 | 61.0352 | 0.00648204 | 1.42857 |
| 4 | 183.105 | 0.00478527 | 4.28571 |
ball fault (overhang/ball_fault/20g/36.0448.csv) (Y axis @ 1s)
- Fundamental frequency: 347.90039062499994 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 347.9 | 1.13694 | 1 |
| 1 | 70.1904 | 0.249652 | 0.201754 |
| 2 | 213.623 | 0.144129 | 0.614035 |
| 3 | 106.812 | 0.0789232 | 0.307018 |
| 4 | 256.348 | 0.0629811 | 0.736842 |
outer race fault (overhang/outer_race/35g/43.2128.csv) (Y axis @ 1s)
- Fundamental frequency: 42.72460937499999 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 42.7246 | 0.14479 | 1 |
| 1 | 256.348 | 0.0180443 | 6 |
| 2 | 12.207 | 0.00889324 | 0.285714 |
| 3 | 344.849 | 0.00475763 | 8.07143 |
| 4 | 430.298 | 0.00405999 | 10.0714 |
Spectrogram in Z axis
z_spectra = get_spectrograms(DATASET, 'z')
normal (normal/43.6224.csv) (Z axis, Fundamental = 0.4000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
misalignment (horizontal-misalignment/2.0mm/42.5984.csv) (Z axis, Fundamental = 0.6000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
imbalance (imbalance/35g/43.6224.csv) (Z axis, Fundamental = 42.6000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
cage fault (overhang/cage_fault/35g/43.008.csv) (Z axis, Fundamental = 42.0000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
ball fault (overhang/ball_fault/20g/36.0448.csv) (Z axis, Fundamental = 35.2000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
outer race fault (overhang/outer_race/35g/43.2128.csv) (Z axis, Fundamental = 42.2000 Hz)
Window size: 16384 Heinsenberg box Time step: 327.68 ms Frequency step: 3.0517578125 Hz
Spectrogram detail in Z axis
show_spectrogram_detail(z_spectra, 'z', T_SEC)
normal (normal/43.6224.csv) (Z axis @ 1s)
misalignment (horizontal-misalignment/2.0mm/42.5984.csv) (Z axis @ 1s)
imbalance (imbalance/35g/43.6224.csv) (Z axis @ 1s)
cage fault (overhang/cage_fault/35g/43.008.csv) (Z axis @ 1s)
ball fault (overhang/ball_fault/20g/36.0448.csv) (Z axis @ 1s)
outer race fault (overhang/outer_race/35g/43.2128.csv) (Z axis @ 1s)
Peaks in frequency spectrum in Z axis
show_mms_peaks(z_spectra, 'z', T_SEC)
normal (normal/43.6224.csv) (Z axis @ 1s)
misalignment (horizontal-misalignment/2.0mm/42.5984.csv) (Z axis @ 1s)
imbalance (imbalance/35g/43.6224.csv) (Z axis @ 1s)
cage fault (overhang/cage_fault/35g/43.008.csv) (Z axis @ 1s)
ball fault (overhang/ball_fault/20g/36.0448.csv) (Z axis @ 1s)
outer race fault (overhang/outer_race/35g/43.2128.csv) (Z axis @ 1s)
Harmonic series detection in Z axis
# show_harmonic_series(z_spectra, 'z', T_SEC)
show_spectra_largest_amplitudes(z_spectra, 'z', T_SEC)
normal (normal/43.6224.csv) (Z axis @ 1s)
- Fundamental frequency: 42.72460937499999 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 42.7246 | 0.140939 | 1 |
| 1 | 2706.91 | 0.0292876 | 63.3571 |
| 2 | 424.194 | 0.0229368 | 9.92857 |
| 3 | 115.967 | 0.0209491 | 2.71429 |
| 4 | 2496.34 | 0.0165197 | 58.4286 |
misalignment (horizontal-misalignment/2.0mm/42.5984.csv) (Z axis @ 1s)
- Fundamental frequency: 42.72460937499999 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 42.7246 | 0.129175 | 1 |
| 1 | 125.122 | 0.0385745 | 2.92857 |
| 2 | 3421.02 | 0.0225597 | 80.0714 |
| 3 | 3469.85 | 0.0203046 | 81.2143 |
| 4 | 2966.31 | 0.016839 | 69.4286 |
imbalance (imbalance/35g/43.6224.csv) (Z axis @ 1s)
- Fundamental frequency: 42.72460937499999 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 42.7246 | 1.31154 | 1 |
| 1 | 6.10352 | 0.0558989 | 0.142857 |
| 2 | 515.747 | 0.0288995 | 12.0714 |
| 3 | 253.296 | 0.0202677 | 5.92857 |
| 4 | 2771 | 0.0182706 | 64.8571 |
cage fault (overhang/cage_fault/35g/43.008.csv) (Z axis @ 1s)
- Fundamental frequency: 42.72460937499999 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 42.7246 | 1.27444 | 1 |
| 1 | 503.54 | 0.0957012 | 11.7857 |
| 2 | 1007.08 | 0.0404181 | 23.5714 |
| 3 | 924.683 | 0.0261103 | 21.6429 |
| 4 | 1049.8 | 0.0202111 | 24.5714 |
ball fault (overhang/ball_fault/20g/36.0448.csv) (Z axis @ 1s)
- Fundamental frequency: 36.62109374999999 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 36.6211 | 0.736213 | 1 |
| 1 | 1080.32 | 0.10298 | 29.5 |
| 2 | 247.192 | 0.0780015 | 6.75 |
| 3 | 1116.94 | 0.0704766 | 30.5 |
| 4 | 70.1904 | 0.0567678 | 1.91667 |
outer race fault (overhang/outer_race/35g/43.2128.csv) (Z axis @ 1s)
- Fundamental frequency: 42.72460937499999 Hz
| f | y | 1x | |
|---|---|---|---|
| 0 | 42.7246 | 1.25633 | 1 |
| 1 | 503.54 | 0.0663159 | 11.7857 |
| 2 | 967.407 | 0.0507056 | 22.6429 |
| 3 | 546.265 | 0.0313838 | 12.7857 |
| 4 | 1000.98 | 0.0270391 | 23.4286 |
Histogram
axis = ['x', 'y', 'z']
for name, ts in DATASET:
display(Markdown(f'**{name}**'))
ts[axis].hist(figsize=(10, 5), grid=True, bins=50)
plt.show()
normal (normal/43.6224.csv)
misalignment (horizontal-misalignment/2.0mm/42.5984.csv)
imbalance (imbalance/35g/43.6224.csv)
cage fault (overhang/cage_fault/35g/43.008.csv)
ball fault (overhang/ball_fault/20g/36.0448.csv)
outer race fault (overhang/outer_race/35g/43.2128.csv)
axis = ['x', 'y', 'z']
for name, ts in DATASET:
display(Markdown(f'**{name}**'))
ts[axis].boxplot(figsize=(10, 5))
plt.show()
normal (normal/43.6224.csv)
misalignment (horizontal-misalignment/2.0mm/42.5984.csv)
imbalance (imbalance/35g/43.6224.csv)
cage fault (overhang/cage_fault/35g/43.008.csv)
ball fault (overhang/ball_fault/20g/36.0448.csv)
outer race fault (overhang/outer_race/35g/43.2128.csv)
Orbitals of all cross sections
for name, ts in DATASET:
display(Markdown(f'**{name}**'))
fig, ax = plt.subplots(1, 3, figsize=(20, 4))
for i, col in enumerate([('x', 'y'), ('x', 'z'), ('y', 'z')]):
ax[i].scatter(ts[col[0]], ts[col[1]], s=1)
ax[i].grid(True)
ax[i].set_xlabel(col[0].upper())
ax[i].set_ylabel(col[1].upper())
ax[i].grid(True)
plt.show() # plt.savefig('orbitals.png')
normal (normal/43.6224.csv)
misalignment (horizontal-misalignment/2.0mm/42.5984.csv)
imbalance (imbalance/35g/43.6224.csv)
cage fault (overhang/cage_fault/35g/43.008.csv)
ball fault (overhang/ball_fault/20g/36.0448.csv)
outer race fault (overhang/outer_race/35g/43.2128.csv)
Orbitals of 1x harmonic frequency
x_spectra_by_name = {spec[0]: spec for spec in x_spectra}
y_spectra_by_name = {spec[0]: spec for spec in y_spectra}
z_spectra_by_name = {spec[0]: spec for spec in z_spectra}
t = 5
space = 5
for name, ts in DATASET:
display(Markdown(f'**{name}**'))
fig, ax = plt.subplots(1, 3, figsize=(20, 4))
name, freqs, Pxx = x_spectra_by_name[name]
x_fundamental = get_max_frequency(freqs, Pxx, window_idx(t))
name, freqs, Pxx = y_spectra_by_name[name]
y_fundamental = get_max_frequency(freqs, Pxx, window_idx(t))
name, freqs, Pxx = z_spectra_by_name[name]
z_fundamental = get_max_frequency(freqs, Pxx, window_idx(t))
try:
ts['x_1x'] = butter_bandpass_filter(ts['x'], x_fundamental - space, x_fundamental + space, Fs)
ts['y_1x'] = butter_bandpass_filter(ts['y'], y_fundamental - space, y_fundamental + space, Fs)
ts['z_1x'] = butter_bandpass_filter(ts['z'], z_fundamental - space, z_fundamental + space, Fs)
except ValueError:
continue
for i, col in enumerate([('x_1x', 'y_1x'), ('x_1x', 'z_1x'), ('y_1x', 'z_1x')]):
ax[i].scatter(ts[col[0]], ts[col[1]], s=1)
ax[i].grid(True)
ax[i].set_xlabel(col[0].upper())
ax[i].set_ylabel(col[1].upper())
ax[i].grid(True)
plt.show() # plt.savefig('orbitals_1x.png')
normal (normal/43.6224.csv)
--------------------------------------------------------------------------- IndexError Traceback (most recent call last) /home/miroslav/fiit-stu/Ing/3-semester/DP/masters-thesis/notebooks/ExploratoryAnalysis/EDA.ipynb Cell 63 line 1 <a href='vscode-notebook-cell:/home/miroslav/fiit-stu/Ing/3-semester/DP/masters-thesis/notebooks/ExploratoryAnalysis/EDA.ipynb#Y114sZmlsZQ%3D%3D?line=8'>9</a> fig, ax = plt.subplots(1, 3, figsize=(20, 4)) <a href='vscode-notebook-cell:/home/miroslav/fiit-stu/Ing/3-semester/DP/masters-thesis/notebooks/ExploratoryAnalysis/EDA.ipynb#Y114sZmlsZQ%3D%3D?line=10'>11</a> name, freqs, Pxx = x_spectra_by_name[name] ---> <a href='vscode-notebook-cell:/home/miroslav/fiit-stu/Ing/3-semester/DP/masters-thesis/notebooks/ExploratoryAnalysis/EDA.ipynb#Y114sZmlsZQ%3D%3D?line=11'>12</a> x_fundamental = get_max_frequency(freqs, Pxx, window_idx(t)) <a href='vscode-notebook-cell:/home/miroslav/fiit-stu/Ing/3-semester/DP/masters-thesis/notebooks/ExploratoryAnalysis/EDA.ipynb#Y114sZmlsZQ%3D%3D?line=12'>13</a> name, freqs, Pxx = y_spectra_by_name[name] <a href='vscode-notebook-cell:/home/miroslav/fiit-stu/Ing/3-semester/DP/masters-thesis/notebooks/ExploratoryAnalysis/EDA.ipynb#Y114sZmlsZQ%3D%3D?line=13'>14</a> y_fundamental = get_max_frequency(freqs, Pxx, window_idx(t)) /home/miroslav/fiit-stu/Ing/3-semester/DP/masters-thesis/notebooks/ExploratoryAnalysis/EDA.ipynb Cell 63 line 3 <a href='vscode-notebook-cell:/home/miroslav/fiit-stu/Ing/3-semester/DP/masters-thesis/notebooks/ExploratoryAnalysis/EDA.ipynb#Y114sZmlsZQ%3D%3D?line=37'>38</a> def get_max_frequency(freqs, Pxx, i): ---> <a href='vscode-notebook-cell:/home/miroslav/fiit-stu/Ing/3-semester/DP/masters-thesis/notebooks/ExploratoryAnalysis/EDA.ipynb#Y114sZmlsZQ%3D%3D?line=38'>39</a> max_freq = freqs[np.argmax(Pxx.T[i])] <a href='vscode-notebook-cell:/home/miroslav/fiit-stu/Ing/3-semester/DP/masters-thesis/notebooks/ExploratoryAnalysis/EDA.ipynb#Y114sZmlsZQ%3D%3D?line=39'>40</a> return max_freq IndexError: index 16 is out of bounds for axis 0 with size 15
t = 5
space = 8
for name, ts in DATASET:
display(Markdown(f'**{name}**'))
name, freqs, Pxx = x_spectra_by_name[name]
x_fundamental = get_max_frequency(freqs, Pxx, window_idx(t))
name, freqs, Pxx = y_spectra_by_name[name]
y_fundamental = get_max_frequency(freqs, Pxx, window_idx(t))
name, freqs, Pxx = z_spectra_by_name[name]
z_fundamental = get_max_frequency(freqs, Pxx, window_idx(t))
try:
x = butter_bandpass_filter(ts['x'], x_fundamental - space, x_fundamental + space, Fs)
y = butter_bandpass_filter(ts['y'], y_fundamental - space, y_fundamental + space, Fs)
z = butter_bandpass_filter(ts['z'], z_fundamental - space, z_fundamental + space, Fs)
except ValueError:
continue
ax = plt.figure().add_subplot(projection='3d')
ax.scatter(x, y, z, zdir='z', s=1, color='navy')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
ax.set_xlim(-2, 2)
ax.set_ylim(-2, 2)
ax.set_zlim(-2, 2)
ax.zaxis.labelpad = -0.7
plt.show()